Efficient processing of Differential Dynamic Microscopy (DDM) allowing both traditional CPU-based, and GPU-accelerated processing, with a GUI for analysis.

Cary Symes (s3550167@student.rmit.edu.au)
Lionel Penington (s3604656@student.rmit.edu.au)

GitHub address: https://github.com/CSymes/quickDDM

Release 1.0 - 03-JUNE-2019
Changes since last release:
This is the first release.

To install the source code, please install Python 3.6 or greater. Ensure that at least the following modules (or more recent versions thereof) are installed:

matplotlib==3.0.3
numpy==1.16.2
opencv-python==4.0.0.21
Pillow==6.0.0
scipy==1.3.0

[Optionally for OpenCL utilisation]
pyopencl==2018.2.5
reikna==0.7.2

These may be installed easily by running pip install -r requirements.txt from in the source directory, assuming pip is already installed. It is recommended that a virtual environment is used to separate this from the system environment.

To launch the UI from the command line, navigate to the project directory and execute python launcher.py.
Alternatively, download and run a binary build from Releases or a build artifact from Azure.

There are a series of tests in the tests directory. They can be run by calling python -m unittest [-v].
Individual tests may be run as such: (e.g.) python -m unittest tests.testFourierTransforms

If you need a binary build, run publish.py from the project root. Creates a portable build of the program in a dist folder, with all necessary libraries bundled. It can be run by executing the quickDDM.exe file inside it.
Requires the PyInstaller packaged to be installed.

This project incorporates concepts from:

G. Cerchiari, F. Croccolo, F. Cardinaux, and F. Scheffold, "Note: Quasi-real-time analysis of dynamic near field scattering data using a graphics processing unit," (in English), Review of Scientific Instruments, Article vol. 83, no. 10, p. 3, Oct 2012, Art no. 106101, doi: 10.1063/1.4755747
The flow of "sequentialChunkerMain" in "processingCore.py" and "sequentialGPUChunker" in "gpuCore.py" in paricular are closely based on the GPU memory management technique detailed therein.

Our understanding of the core process was heavily informed by:
L. G. Wilson et al., "Differential Dynamic Microscopy of Bacterial Motility," Physical Review Letters, vol. 106, no. 1, p. 4, Jan 2011.